While gesture taxonomies provide a classification of device-based gestures in terms of communicative intent, little work has addressed the usability differences in manually performing these gestures. In this primarily qualitative study, we investigate how two sets of iconic gestures that vary in familiarity, mimetic and alphabetic, are affected under varying failed

ForcePhone is a mobile synchronous haptic communication system. During phone calls, users can squeeze the side of the device and the pressure level is mapped to vibrations on the recipient's device. The pressure/vibrotactile messages supported by ForcePhone are called pressages. Using a lab-based study and a small field study, this paper addresses the following questions: how can haptic interpersonal communication be integrated into a standard mobile device? What is the most appropriate feedback design for pressages? What types of non-verbal cues can be represented by pressages? Do users make use of pressages during their conversations? The results of this research indicate that such a system has value as a communication channel in real-world settings with users expressing greetings, presence and emotions through pressages.

This paper describes a novel hand gesture recognition system that utilizes both multi-channel surface electromyogram (EMG) sensors and 3D accelerometer (ACC) to realize user-friendly interaction between human and computers. Signal segments of meaningful gestures are determined from the continuous EMG signal inputs. Multi-stream Hidden Markov Models consisting of EMG and ACC streams are utilized as decision fusion method to recognize hand gestures. This paper also presents a virtual Rubik's Cube game that is controlled by the hand gestures and is used for evaluating the performance of our hand gesture recognition system. For a set of 18 kinds of gestures, each trained with 10 repetitions, the average recognition accuracy was about 91.7% in real application. The proposed method facilitates intelligent and natural control based on gesture interaction.

In this paper we present results of a study where perception of dynamic audiotactile feedback to gesture input was examined. Our main motivation was to investigate how users' active input and different modality conditions effect the perception of the feedback. The experimental prototype in the study was a handheld sensor-actuator device that responds dynamically to user's hand movements creating an impression of a virtual texture. The feedback was designed so that the amplitude and frequency of texture were proportional to the overall angular velocity of the device. We used four different textures with different velocity responses. The feedback was presented to the user by the tactile actuator in the device, by audio through headphones, or by both. During the experiments, textures were switched in random intervals and the task of the user was to detect the changes while moving the device freely. The performances of the users with audio or audiotactile feedback were quite equal while tactile feedback alone yielded poorer performance. The texture design didn't influence the movement velocity or periodicity but tactile feedback induced most and audio feedback the least energetic motion. In addition, significantly better performance was achieved with slower motion. We also found that significant learning happened over time; detection accuracy increased significantly during and between the experiments. The masking noise used in tactile modality condition did not significantly influence the detection accuracy when compared to acoustic blocking but it increased the average detection time.

Handwriting recognition (HWR) input method has been considered to be one of the most usable text entry methods for handheld devices, especially for languages with large and complicated character sets such as Chinese. The paper studies stroke break times within handwritten characters and presents a new method for setting HWR timeout by examining the break time distributions. For multi-stroke character HWR input, a timeout is widely used as a segmentation technique to initiate the recognition process. In this paper, we examine the largest stroke break time in each character and explore the relationship between break time distribution and optimal HWR timeout. The study used Chinese as test material and the test independent variables were writing condition (input box, full screen) and user's posture while they were writing (hold device in hand, keep device on table). The main findings are: (1) the stroke break times are similar in full screen and input box conditions, though the users tend to write larger characters in full screen condition. (2) The stroke break times fit into a tight distribution. It is feasible to estimate optimal HWR timeout by studying stoke break time distribution. A nonparametric histogram method was used to model the stroke break distributions and it showed that typical Chinese HWR default timeouts are around 99% percentile in the distribution. (3) Differences in HWR stroke break distributions are very significant between individual users. The stroke break time analysis can also be applied to design HWR timeout customization scale.

We describe a rhythmic interaction mechanism for mobile devices. A PocketPC with a three degree of freedom linear acceleration meter is used as the experimental platform for data acquisition. Dynamic Movement Primitives are used to learn the limit cycle behavior associated with the rhythmic gestures. We outline the open technical and user experience challenges in the development of usable rhythmic interfaces.